Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations63081
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.1 MiB
Average record size in memory152.0 B

Variable types

Text6
Categorical5
Numeric7

Alerts

Clean Alternative Fuel Vehicle (CAFV) Eligibility is highly overall correlated with Electric Range and 3 other fieldsHigh correlation
County is highly overall correlated with Legislative District and 2 other fieldsHigh correlation
Electric Range is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 1 other fieldsHigh correlation
Electric Vehicle Type is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 3 other fieldsHigh correlation
Expected Price ($1k) is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 3 other fieldsHigh correlation
Legislative District is highly overall correlated with CountyHigh correlation
Make is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 2 other fieldsHigh correlation
Model Year is highly overall correlated with Expected Price ($1k)High correlation
State is highly overall correlated with County and 1 other fieldsHigh correlation
ZIP Code is highly overall correlated with County and 1 other fieldsHigh correlation
State is highly imbalanced (99.9%)Imbalance
ZIP Code is highly skewed (γ1 = -23.35409993)Skewed
ID has unique valuesUnique
DOL Vehicle ID has unique valuesUnique
Electric Range has 14657 (23.2%) zerosZeros
Base MSRP has 60053 (95.2%) zerosZeros

Reproduction

Analysis started2024-12-02 14:41:52.835105
Analysis finished2024-12-02 14:42:58.664054
Duration1 minute and 5.83 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

ID
Text

UNIQUE 

Distinct63081
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:00.207832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8818345
Min length3

Characters and Unicode

Total characters434113
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63081 ?
Unique (%)100.0%

Sample

1st rowEV33174
2nd rowEV40247
3rd rowEV12248
4th rowEV55713
5th rowEV28799
ValueCountFrequency (%)
ev33174 1
 
< 0.1%
ev61786 1
 
< 0.1%
ev60210 1
 
< 0.1%
ev84036 1
 
< 0.1%
ev52059 1
 
< 0.1%
ev12248 1
 
< 0.1%
ev55713 1
 
< 0.1%
ev28799 1
 
< 0.1%
ev49859 1
 
< 0.1%
ev53121 1
 
< 0.1%
Other values (63071) 63071
> 99.9%
2024-12-02T15:43:02.934154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 63081
14.5%
V 63081
14.5%
1 32648
7.5%
4 32078
7.4%
2 32058
7.4%
8 32018
7.4%
3 32011
7.4%
7 31937
7.4%
5 31894
7.3%
6 31843
7.3%
Other values (2) 51464
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 434113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 63081
14.5%
V 63081
14.5%
1 32648
7.5%
4 32078
7.4%
2 32058
7.4%
8 32018
7.4%
3 32011
7.4%
7 31937
7.4%
5 31894
7.3%
6 31843
7.3%
Other values (2) 51464
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 434113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 63081
14.5%
V 63081
14.5%
1 32648
7.5%
4 32078
7.4%
2 32058
7.4%
8 32018
7.4%
3 32011
7.4%
7 31937
7.4%
5 31894
7.3%
6 31843
7.3%
Other values (2) 51464
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 434113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 63081
14.5%
V 63081
14.5%
1 32648
7.5%
4 32078
7.4%
2 32058
7.4%
8 32018
7.4%
3 32011
7.4%
7 31937
7.4%
5 31894
7.3%
6 31843
7.3%
Other values (2) 51464
11.9%
Distinct5592
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:03.684001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters630810
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1260 ?
Unique (%)2.0%

Sample

1st row5YJ3E1EC6L
2nd rowJN1AZ0CP8B
3rd rowWBY1Z2C56F
4th row1G1RD6E44D
5th row1G1FY6S05K
ValueCountFrequency (%)
5yjygdee9m 334
 
0.5%
5yjygdee8m 331
 
0.5%
5yjygdee0m 325
 
0.5%
5yjygdee6m 318
 
0.5%
5yjygdee2m 311
 
0.5%
5yjygdee4m 300
 
0.5%
5yjygdee7m 298
 
0.5%
5yjygdeexm 297
 
0.5%
5yjygdee5m 286
 
0.5%
5yjygdee1m 285
 
0.5%
Other values (5582) 59996
95.1%
2024-12-02T15:43:05.064695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 55974
 
8.9%
E 54714
 
8.7%
J 42662
 
6.8%
5 38820
 
6.2%
Y 37393
 
5.9%
A 31500
 
5.0%
3 27859
 
4.4%
C 26256
 
4.2%
D 21759
 
3.4%
G 21377
 
3.4%
Other values (23) 272496
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 630810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 55974
 
8.9%
E 54714
 
8.7%
J 42662
 
6.8%
5 38820
 
6.2%
Y 37393
 
5.9%
A 31500
 
5.0%
3 27859
 
4.4%
C 26256
 
4.2%
D 21759
 
3.4%
G 21377
 
3.4%
Other values (23) 272496
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 630810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 55974
 
8.9%
E 54714
 
8.7%
J 42662
 
6.8%
5 38820
 
6.2%
Y 37393
 
5.9%
A 31500
 
5.0%
3 27859
 
4.4%
C 26256
 
4.2%
D 21759
 
3.4%
G 21377
 
3.4%
Other values (23) 272496
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 630810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 55974
 
8.9%
E 54714
 
8.7%
J 42662
 
6.8%
5 38820
 
6.2%
Y 37393
 
5.9%
A 31500
 
5.0%
3 27859
 
4.4%
C 26256
 
4.2%
D 21759
 
3.4%
G 21377
 
3.4%
Other values (23) 272496
43.2%

County
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
King
33121 
Snohomish
6908 
Pierce
4822 
Clark
3762 
Thurston
 
2436
Other values (36)
12032 

Length

Max length12
Median length4
Mean length5.4326025
Min length4

Characters and Unicode

Total characters342694
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowSnohomish
2nd rowSkagit
3rd rowPierce
4th rowKing
5th rowPierce

Common Values

ValueCountFrequency (%)
King 33121
52.5%
Snohomish 6908
 
11.0%
Pierce 4822
 
7.6%
Clark 3762
 
6.0%
Thurston 2436
 
3.9%
Kitsap 2291
 
3.6%
Whatcom 1713
 
2.7%
Spokane 1487
 
2.4%
Benton 811
 
1.3%
Island 766
 
1.2%
Other values (31) 4964
 
7.9%

Length

2024-12-02T15:43:05.501959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
king 33121
51.9%
snohomish 6908
 
10.8%
pierce 4822
 
7.6%
clark 3762
 
5.9%
thurston 2436
 
3.8%
kitsap 2291
 
3.6%
whatcom 1713
 
2.7%
spokane 1487
 
2.3%
benton 811
 
1.3%
island 766
 
1.2%
Other values (34) 5643
 
8.9%

Most occurring characters

ValueCountFrequency (%)
i 49842
14.5%
n 49309
14.4%
K 35720
10.4%
g 34090
9.9%
o 21825
 
6.4%
h 18461
 
5.4%
a 15200
 
4.4%
s 14092
 
4.1%
e 13584
 
4.0%
r 12545
 
3.7%
Other values (32) 78026
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 342694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 49842
14.5%
n 49309
14.4%
K 35720
10.4%
g 34090
9.9%
o 21825
 
6.4%
h 18461
 
5.4%
a 15200
 
4.4%
s 14092
 
4.1%
e 13584
 
4.0%
r 12545
 
3.7%
Other values (32) 78026
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 342694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 49842
14.5%
n 49309
14.4%
K 35720
10.4%
g 34090
9.9%
o 21825
 
6.4%
h 18461
 
5.4%
a 15200
 
4.4%
s 14092
 
4.1%
e 13584
 
4.0%
r 12545
 
3.7%
Other values (32) 78026
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 342694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 49842
14.5%
n 49309
14.4%
K 35720
10.4%
g 34090
9.9%
o 21825
 
6.4%
h 18461
 
5.4%
a 15200
 
4.4%
s 14092
 
4.1%
e 13584
 
4.0%
r 12545
 
3.7%
Other values (32) 78026
22.8%

City
Text

Distinct398
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:06.517048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length17
Mean length8.2492034
Min length3

Characters and Unicode

Total characters520368
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)0.1%

Sample

1st rowLYNNWOOD
2nd rowBELLINGHAM
3rd rowTACOMA
4th rowREDMOND
5th rowPUYALLUP
ValueCountFrequency (%)
seattle 11463
 
15.7%
bellevue 3353
 
4.6%
redmond 2448
 
3.4%
vancouver 2301
 
3.2%
kirkland 2086
 
2.9%
island 2072
 
2.8%
sammamish 1874
 
2.6%
bothell 1769
 
2.4%
olympia 1545
 
2.1%
renton 1512
 
2.1%
Other values (419) 42557
58.3%
2024-12-02T15:43:08.446052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 74900
14.4%
A 51412
 
9.9%
L 49545
 
9.5%
T 39353
 
7.6%
N 35373
 
6.8%
O 32778
 
6.3%
S 31962
 
6.1%
R 27384
 
5.3%
I 23427
 
4.5%
M 20829
 
4.0%
Other values (17) 133405
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 74900
14.4%
A 51412
 
9.9%
L 49545
 
9.5%
T 39353
 
7.6%
N 35373
 
6.8%
O 32778
 
6.3%
S 31962
 
6.1%
R 27384
 
5.3%
I 23427
 
4.5%
M 20829
 
4.0%
Other values (17) 133405
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 74900
14.4%
A 51412
 
9.9%
L 49545
 
9.5%
T 39353
 
7.6%
N 35373
 
6.8%
O 32778
 
6.3%
S 31962
 
6.1%
R 27384
 
5.3%
I 23427
 
4.5%
M 20829
 
4.0%
Other values (17) 133405
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 74900
14.4%
A 51412
 
9.9%
L 49545
 
9.5%
T 39353
 
7.6%
N 35373
 
6.8%
O 32778
 
6.3%
S 31962
 
6.1%
R 27384
 
5.3%
I 23427
 
4.5%
M 20829
 
4.0%
Other values (17) 133405
25.6%

State
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
WA
63076 
OR
 
4
MT
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters126162
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 63076
> 99.9%
OR 4
 
< 0.1%
MT 1
 
< 0.1%

Length

2024-12-02T15:43:08.947461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T15:43:09.386690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wa 63076
> 99.9%
or 4
 
< 0.1%
mt 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
W 63076
50.0%
A 63076
50.0%
O 4
 
< 0.1%
R 4
 
< 0.1%
M 1
 
< 0.1%
T 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 63076
50.0%
A 63076
50.0%
O 4
 
< 0.1%
R 4
 
< 0.1%
M 1
 
< 0.1%
T 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 63076
50.0%
A 63076
50.0%
O 4
 
< 0.1%
R 4
 
< 0.1%
M 1
 
< 0.1%
T 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 63076
50.0%
A 63076
50.0%
O 4
 
< 0.1%
R 4
 
< 0.1%
M 1
 
< 0.1%
T 1
 
< 0.1%

ZIP Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct501
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98252.382
Minimum59937
Maximum99403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:10.280575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum59937
5-th percentile98006
Q198052
median98119
Q398368
95-th percentile98837
Maximum99403
Range39466
Interquartile range (IQR)316

Descriptive statistics

Standard deviation330.7156
Coefficient of variation (CV)0.0033659805
Kurtosis2857.4132
Mean98252.382
Median Absolute Deviation (MAD)98
Skewness-23.3541
Sum6.1978585 × 109
Variance109372.81
MonotonicityNot monotonic
2024-12-02T15:43:11.300916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 1710
 
2.7%
98033 1223
 
1.9%
98004 1140
 
1.8%
98115 1104
 
1.8%
98012 1019
 
1.6%
98006 1006
 
1.6%
98040 975
 
1.5%
98074 933
 
1.5%
98103 896
 
1.4%
98034 862
 
1.4%
Other values (491) 52213
82.8%
ValueCountFrequency (%)
59937 1
 
< 0.1%
97132 1
 
< 0.1%
97202 1
 
< 0.1%
97239 1
 
< 0.1%
97436 1
 
< 0.1%
98001 258
 
0.4%
98002 97
 
0.2%
98003 163
 
0.3%
98004 1140
1.8%
98005 495
0.8%
ValueCountFrequency (%)
99403 25
 
< 0.1%
99402 6
 
< 0.1%
99362 2
 
< 0.1%
99361 1
 
< 0.1%
99357 7
 
< 0.1%
99356 2
 
< 0.1%
99354 126
0.2%
99353 86
 
0.1%
99352 218
0.3%
99350 20
 
< 0.1%

Model Year
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.1871
Minimum1993
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:11.665205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1993
5-th percentile2013
Q12017
median2018
Q32021
95-th percentile2022
Maximum2022
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7251924
Coefficient of variation (CV)0.001350317
Kurtosis-0.03658288
Mean2018.1871
Median Absolute Deviation (MAD)2
Skewness-0.67921201
Sum1.2730926 × 108
Variance7.4266734
MonotonicityNot monotonic
2024-12-02T15:43:12.045653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2021 12922
20.5%
2018 9574
15.2%
2020 7363
11.7%
2019 7248
11.5%
2017 6699
10.6%
2016 4317
 
6.8%
2022 3819
 
6.1%
2015 3428
 
5.4%
2013 3289
 
5.2%
2014 2593
 
4.1%
Other values (9) 1829
 
2.9%
ValueCountFrequency (%)
1993 1
 
< 0.1%
1998 1
 
< 0.1%
1999 2
 
< 0.1%
2000 4
 
< 0.1%
2002 2
 
< 0.1%
2008 14
 
< 0.1%
2010 14
 
< 0.1%
2011 594
 
0.9%
2012 1197
 
1.9%
2013 3289
5.2%
ValueCountFrequency (%)
2022 3819
 
6.1%
2021 12922
20.5%
2020 7363
11.7%
2019 7248
11.5%
2018 9574
15.2%
2017 6699
10.6%
2016 4317
 
6.8%
2015 3428
 
5.4%
2014 2593
 
4.1%
2013 3289
 
5.2%

Make
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
TESLA
27398 
NISSAN
8548 
CHEVROLET
6494 
FORD
3752 
KIA
3005 
Other values (29)
13884 

Length

Max length20
Median length14
Mean length5.5760847
Min length3

Characters and Unicode

Total characters351745
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowTESLA
2nd rowNISSAN
3rd rowBMW
4th rowCHEVROLET
5th rowCHEVROLET

Common Values

ValueCountFrequency (%)
TESLA 27398
43.4%
NISSAN 8548
 
13.6%
CHEVROLET 6494
 
10.3%
FORD 3752
 
5.9%
KIA 3005
 
4.8%
BMW 2649
 
4.2%
TOYOTA 2633
 
4.2%
AUDI 1236
 
2.0%
VOLKSWAGEN 1183
 
1.9%
CHRYSLER 1069
 
1.7%
Other values (24) 5114
 
8.1%

Length

2024-12-02T15:43:12.515519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 27398
43.4%
nissan 8548
 
13.5%
chevrolet 6494
 
10.3%
ford 3752
 
5.9%
kia 3005
 
4.8%
bmw 2649
 
4.2%
toyota 2633
 
4.2%
audi 1236
 
2.0%
volkswagen 1183
 
1.9%
chrysler 1069
 
1.7%
Other values (29) 5146
 
8.2%

Most occurring characters

ValueCountFrequency (%)
S 48580
13.8%
A 46791
13.3%
E 45112
12.8%
T 40434
11.5%
L 37264
10.6%
N 20378
 
5.8%
O 19270
 
5.5%
I 16102
 
4.6%
R 13695
 
3.9%
H 9776
 
2.8%
Other values (17) 54343
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 351745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 48580
13.8%
A 46791
13.3%
E 45112
12.8%
T 40434
11.5%
L 37264
10.6%
N 20378
 
5.8%
O 19270
 
5.5%
I 16102
 
4.6%
R 13695
 
3.9%
H 9776
 
2.8%
Other values (17) 54343
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 351745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 48580
13.8%
A 46791
13.3%
E 45112
12.8%
T 40434
11.5%
L 37264
10.6%
N 20378
 
5.8%
O 19270
 
5.5%
I 16102
 
4.6%
R 13695
 
3.9%
H 9776
 
2.8%
Other values (17) 54343
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 351745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 48580
13.8%
A 46791
13.3%
E 45112
12.8%
T 40434
11.5%
L 37264
10.6%
N 20378
 
5.8%
O 19270
 
5.5%
I 16102
 
4.6%
R 13695
 
3.9%
H 9776
 
2.8%
Other values (17) 54343
15.4%

Model
Text

Distinct107
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:13.278198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.2682107
Min length2

Characters and Unicode

Total characters395405
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowMODEL 3
2nd rowLEAF
3rd rowI3
4th rowVOLT
5th rowBOLT EV
ValueCountFrequency (%)
model 27364
27.7%
3 12889
13.1%
leaf 8548
 
8.7%
y 7483
 
7.6%
s 4629
 
4.7%
volt 3326
 
3.4%
ev 3159
 
3.2%
bolt 2984
 
3.0%
x 2363
 
2.4%
niro 2304
 
2.3%
Other values (97) 23613
23.9%
2024-12-02T15:43:14.627577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 46619
11.8%
E 46328
11.7%
O 41566
10.5%
35581
 
9.0%
M 31192
 
7.9%
D 29385
 
7.4%
A 17845
 
4.5%
3 15315
 
3.9%
I 15150
 
3.8%
F 12681
 
3.2%
Other values (28) 103743
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 395405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 46619
11.8%
E 46328
11.7%
O 41566
10.5%
35581
 
9.0%
M 31192
 
7.9%
D 29385
 
7.4%
A 17845
 
4.5%
3 15315
 
3.9%
I 15150
 
3.8%
F 12681
 
3.2%
Other values (28) 103743
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 395405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 46619
11.8%
E 46328
11.7%
O 41566
10.5%
35581
 
9.0%
M 31192
 
7.9%
D 29385
 
7.4%
A 17845
 
4.5%
3 15315
 
3.9%
I 15150
 
3.8%
F 12681
 
3.2%
Other values (28) 103743
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 395405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 46619
11.8%
E 46328
11.7%
O 41566
10.5%
35581
 
9.0%
M 31192
 
7.9%
D 29385
 
7.4%
A 17845
 
4.5%
3 15315
 
3.9%
I 15150
 
3.8%
F 12681
 
3.2%
Other values (28) 103743
26.2%

Electric Vehicle Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
Battery Electric Vehicle (BEV)
47001 
Plug-in Hybrid Electric Vehicle (PHEV)
16080 

Length

Max length38
Median length30
Mean length32.039283
Min length30

Characters and Unicode

Total characters2021070
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowBattery Electric Vehicle (BEV)
3rd rowBattery Electric Vehicle (BEV)
4th rowPlug-in Hybrid Electric Vehicle (PHEV)
5th rowBattery Electric Vehicle (BEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 47001
74.5%
Plug-in Hybrid Electric Vehicle (PHEV) 16080
 
25.5%

Length

2024-12-02T15:43:15.149752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T15:43:15.901596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
electric 63081
23.5%
vehicle 63081
23.5%
battery 47001
17.5%
bev 47001
17.5%
plug-in 16080
 
6.0%
hybrid 16080
 
6.0%
phev 16080
 
6.0%

Most occurring characters

ValueCountFrequency (%)
e 236244
11.7%
205323
10.2%
c 189243
9.4%
i 158322
 
7.8%
t 157083
 
7.8%
l 142242
 
7.0%
V 126162
 
6.2%
r 126162
 
6.2%
E 126162
 
6.2%
B 94002
 
4.7%
Other values (13) 460125
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2021070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 236244
11.7%
205323
10.2%
c 189243
9.4%
i 158322
 
7.8%
t 157083
 
7.8%
l 142242
 
7.0%
V 126162
 
6.2%
r 126162
 
6.2%
E 126162
 
6.2%
B 94002
 
4.7%
Other values (13) 460125
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2021070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 236244
11.7%
205323
10.2%
c 189243
9.4%
i 158322
 
7.8%
t 157083
 
7.8%
l 142242
 
7.0%
V 126162
 
6.2%
r 126162
 
6.2%
E 126162
 
6.2%
B 94002
 
4.7%
Other values (13) 460125
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2021070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 236244
11.7%
205323
10.2%
c 189243
9.4%
i 158322
 
7.8%
t 157083
 
7.8%
l 142242
 
7.0%
V 126162
 
6.2%
r 126162
 
6.2%
E 126162
 
6.2%
B 94002
 
4.7%
Other values (13) 460125
22.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
Clean Alternative Fuel Vehicle Eligible
39172 
Eligibility unknown as battery range has not been researched
14657 
Not eligible due to low battery range
9252 

Length

Max length60
Median length39
Mean length43.586056
Min length37

Characters and Unicode

Total characters2749452
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowClean Alternative Fuel Vehicle Eligible
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowClean Alternative Fuel Vehicle Eligible
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Clean Alternative Fuel Vehicle Eligible 39172
62.1%
Eligibility unknown as battery range has not been researched 14657
 
23.2%
Not eligible due to low battery range 9252
 
14.7%

Length

2024-12-02T15:43:16.398848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T15:43:16.722947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
eligible 48424
12.3%
clean 39172
10.0%
alternative 39172
10.0%
fuel 39172
10.0%
vehicle 39172
10.0%
not 23909
 
6.1%
range 23909
 
6.1%
battery 23909
 
6.1%
as 14657
 
3.7%
unknown 14657
 
3.7%
Other values (7) 86384
22.0%

Most occurring characters

ValueCountFrequency (%)
e 423063
15.4%
329456
12.0%
l 292102
10.6%
i 233820
 
8.5%
n 175538
 
6.4%
t 173980
 
6.3%
a 170133
 
6.2%
r 116304
 
4.2%
b 101647
 
3.7%
g 86990
 
3.2%
Other values (16) 646419
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2749452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 423063
15.4%
329456
12.0%
l 292102
10.6%
i 233820
 
8.5%
n 175538
 
6.4%
t 173980
 
6.3%
a 170133
 
6.2%
r 116304
 
4.2%
b 101647
 
3.7%
g 86990
 
3.2%
Other values (16) 646419
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2749452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 423063
15.4%
329456
12.0%
l 292102
10.6%
i 233820
 
8.5%
n 175538
 
6.4%
t 173980
 
6.3%
a 170133
 
6.2%
r 116304
 
4.2%
b 101647
 
3.7%
g 86990
 
3.2%
Other values (16) 646419
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2749452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 423063
15.4%
329456
12.0%
l 292102
10.6%
i 233820
 
8.5%
n 175538
 
6.4%
t 173980
 
6.3%
a 170133
 
6.2%
r 116304
 
4.2%
b 101647
 
3.7%
g 86990
 
3.2%
Other values (16) 646419
23.5%

Electric Range
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.08857
Minimum0
Maximum337
Zeros14657
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:17.442958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median73
Q3215
95-th percentile291
Maximum337
Range337
Interquartile range (IQR)201

Descriptive statistics

Standard deviation104.11183
Coefficient of variation (CV)0.972203
Kurtosis-1.2767665
Mean107.08857
Median Absolute Deviation (MAD)73
Skewness0.50763383
Sum6755254
Variance10839.273
MonotonicityNot monotonic
2024-12-02T15:43:18.391568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14657
23.2%
215 4273
 
6.8%
84 2792
 
4.4%
220 2776
 
4.4%
238 2498
 
4.0%
208 1818
 
2.9%
19 1766
 
2.8%
25 1758
 
2.8%
53 1657
 
2.6%
291 1631
 
2.6%
Other values (88) 27455
43.5%
ValueCountFrequency (%)
0 14657
23.2%
6 647
 
1.0%
8 25
 
< 0.1%
9 11
 
< 0.1%
10 95
 
0.2%
11 3
 
< 0.1%
12 98
 
0.2%
13 232
 
0.4%
14 780
 
1.2%
15 48
 
0.1%
ValueCountFrequency (%)
337 40
 
0.1%
330 207
 
0.3%
322 1066
1.7%
308 337
 
0.5%
293 297
 
0.5%
291 1631
2.6%
289 432
 
0.7%
270 162
 
0.3%
266 1026
1.6%
265 104
 
0.2%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2529.5239
Minimum0
Maximum845000
Zeros60053
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:19.423462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12431.057
Coefficient of variation (CV)4.9143861
Kurtosis360.599
Mean2529.5239
Median Absolute Deviation (MAD)0
Skewness9.6182968
Sum1.595649 × 108
Variance1.5453118 × 108
MonotonicityNot monotonic
2024-12-02T15:43:19.912226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 60053
95.2%
69900 1048
 
1.7%
34600 346
 
0.5%
31950 298
 
0.5%
28500 143
 
0.2%
52900 141
 
0.2%
38500 124
 
0.2%
32250 112
 
0.2%
59900 104
 
0.2%
54950 99
 
0.2%
Other values (27) 613
 
1.0%
ValueCountFrequency (%)
0 60053
95.2%
28500 143
 
0.2%
31950 298
 
0.5%
32000 1
 
< 0.1%
32250 112
 
0.2%
32995 1
 
< 0.1%
33950 61
 
0.1%
34600 346
 
0.5%
34995 32
 
0.1%
35390 9
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 9
< 0.1%
110950 14
< 0.1%
109000 6
< 0.1%
102000 10
< 0.1%
98950 14
< 0.1%
91250 1
 
< 0.1%
90700 11
< 0.1%
89100 5
 
< 0.1%
81100 11
< 0.1%

Legislative District
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.060383
Minimum0
Maximum49
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:20.380520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q120
median34
Q343
95-th percentile48
Maximum49
Range49
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.665353
Coefficient of variation (CV)0.48786316
Kurtosis-0.94103506
Mean30.060383
Median Absolute Deviation (MAD)11
Skewness-0.56756768
Sum1896239
Variance215.07259
MonotonicityNot monotonic
2024-12-02T15:43:20.934820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 4290
 
6.8%
45 4159
 
6.6%
48 3783
 
6.0%
46 2781
 
4.4%
36 2753
 
4.4%
43 2623
 
4.2%
1 2605
 
4.1%
5 2604
 
4.1%
37 2090
 
3.3%
34 2039
 
3.2%
Other values (40) 33354
52.9%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 2605
4.1%
2 702
 
1.1%
3 340
 
0.5%
4 389
 
0.6%
5 2604
4.1%
6 580
 
0.9%
7 256
 
0.4%
8 696
 
1.1%
9 337
 
0.5%
ValueCountFrequency (%)
49 929
 
1.5%
48 3783
6.0%
47 976
 
1.5%
46 2781
4.4%
45 4159
6.6%
44 1467
 
2.3%
43 2623
4.2%
42 984
 
1.6%
41 4290
6.8%
40 1558
 
2.5%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct63081
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9737376 × 108
Minimum4385
Maximum4.7893457 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:21.743859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile9145062
Q11.3733115 × 108
median1.7540033 × 108
Q32.3003745 × 108
95-th percentile4.7537746 × 108
Maximum4.7893457 × 108
Range4.7893019 × 108
Interquartile range (IQR)92706306

Descriptive statistics

Standard deviation1.0705463 × 108
Coefficient of variation (CV)0.54239544
Kurtosis1.3220455
Mean1.9737376 × 108
Median Absolute Deviation (MAD)42382991
Skewness1.1053377
Sum1.2450534 × 1013
Variance1.1460693 × 1016
MonotonicityNot monotonic
2024-12-02T15:43:22.246930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109821694 1
 
< 0.1%
157568064 1
 
< 0.1%
103258753 1
 
< 0.1%
109824718 1
 
< 0.1%
184570816 1
 
< 0.1%
214459493 1
 
< 0.1%
175294542 1
 
< 0.1%
170242831 1
 
< 0.1%
192640311 1
 
< 0.1%
186886038 1
 
< 0.1%
Other values (63071) 63071
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
23145 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
61092 1
< 0.1%
ValueCountFrequency (%)
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
478909938 1
< 0.1%
478909224 1
< 0.1%
478909070 1
< 0.1%
Distinct499
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:23.056504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length29
Median length29
Mean length28.83862
Min length21

Characters and Unicode

Total characters1819169
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)0.1%

Sample

1st rowPOINT (-122.287614 47.83874)
2nd rowPOINT (-122.414936 48.709388)
3rd rowPOINT (-122.396286 47.293138)
4th rowPOINT (-122.024951 47.670286)
5th rowPOINT (-122.321062 47.103797)
ValueCountFrequency (%)
point 63081
33.3%
47.678465 1710
 
0.9%
122.122018 1710
 
0.9%
122.188994 1223
 
0.6%
47.678406 1223
 
0.6%
122.132064 1200
 
0.6%
122.203169 1140
 
0.6%
47.619011 1140
 
0.6%
122.297534 1104
 
0.6%
47.685291 1104
 
0.6%
Other values (988) 114608
60.6%
2024-12-02T15:43:24.234122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 190214
 
10.5%
1 152695
 
8.4%
4 146798
 
8.1%
. 126162
 
6.9%
126162
 
6.9%
7 118192
 
6.5%
6 87604
 
4.8%
3 82331
 
4.5%
8 75631
 
4.2%
5 75327
 
4.1%
Other values (10) 638053
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1819169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 190214
 
10.5%
1 152695
 
8.4%
4 146798
 
8.1%
. 126162
 
6.9%
126162
 
6.9%
7 118192
 
6.5%
6 87604
 
4.8%
3 82331
 
4.5%
8 75631
 
4.2%
5 75327
 
4.1%
Other values (10) 638053
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1819169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 190214
 
10.5%
1 152695
 
8.4%
4 146798
 
8.1%
. 126162
 
6.9%
126162
 
6.9%
7 118192
 
6.5%
6 87604
 
4.8%
3 82331
 
4.5%
8 75631
 
4.2%
5 75327
 
4.1%
Other values (10) 638053
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1819169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 190214
 
10.5%
1 152695
 
8.4%
4 146798
 
8.1%
. 126162
 
6.9%
126162
 
6.9%
7 118192
 
6.5%
6 87604
 
4.8%
3 82331
 
4.5%
8 75631
 
4.2%
5 75327
 
4.1%
Other values (10) 638053
35.1%
Distinct68
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:24.844824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.689637
Min length11

Characters and Unicode

Total characters2819067
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowPUGET SOUND ENERGY INC
2nd rowPUGET SOUND ENERGY INC
3rd rowBONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY
4th rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
5th rowBONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||ELMHURST MUTUAL POWER & LIGHT CO|PENINSULA LIGHT COMPANY
ValueCountFrequency (%)
of 60958
12.8%
56800
11.9%
wa 39022
 
8.2%
tacoma 38560
 
8.1%
sound 37313
 
7.8%
energy 37313
 
7.8%
puget 36925
 
7.7%
inc||city 22547
 
4.7%
power 14116
 
3.0%
inc 12860
 
2.7%
Other values (101) 120907
25.3%
2024-12-02T15:43:25.933079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
414240
14.7%
O 208121
 
7.4%
N 199188
 
7.1%
T 197085
 
7.0%
A 191354
 
6.8%
E 183728
 
6.5%
I 152715
 
5.4%
C 152560
 
5.4%
Y 103205
 
3.7%
U 97696
 
3.5%
Other values (26) 919175
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2819067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
414240
14.7%
O 208121
 
7.4%
N 199188
 
7.1%
T 197085
 
7.0%
A 191354
 
6.8%
E 183728
 
6.5%
I 152715
 
5.4%
C 152560
 
5.4%
Y 103205
 
3.7%
U 97696
 
3.5%
Other values (26) 919175
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2819067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
414240
14.7%
O 208121
 
7.4%
N 199188
 
7.1%
T 197085
 
7.0%
A 191354
 
6.8%
E 183728
 
6.5%
I 152715
 
5.4%
C 152560
 
5.4%
Y 103205
 
3.7%
U 97696
 
3.5%
Other values (26) 919175
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2819067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
414240
14.7%
O 208121
 
7.4%
N 199188
 
7.1%
T 197085
 
7.0%
A 191354
 
6.8%
E 183728
 
6.5%
I 152715
 
5.4%
C 152560
 
5.4%
Y 103205
 
3.7%
U 97696
 
3.5%
Other values (26) 919175
32.6%

Expected Price ($1k)
Real number (ℝ)

HIGH CORRELATION 

Distinct208
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.476095
Minimum0
Maximum1100
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size985.6 KiB
2024-12-02T15:43:26.321327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q122.857
median40
Q367.07
95-th percentile78
Maximum1100
Range1100
Interquartile range (IQR)44.213

Descriptive statistics

Standard deviation24.757155
Coefficient of variation (CV)0.54439931
Kurtosis73.314354
Mean45.476095
Median Absolute Deviation (MAD)20
Skewness2.4993769
Sum2868677.5
Variance612.91671
MonotonicityNot monotonic
2024-12-02T15:43:27.264713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 4734
 
7.5%
73 4689
 
7.4%
64 3564
 
5.6%
57 2736
 
4.3%
50 2475
 
3.9%
25 2007
 
3.2%
18 1908
 
3.0%
20 1905
 
3.0%
19 1725
 
2.7%
72 1631
 
2.6%
Other values (198) 35707
56.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
2.8 2
 
< 0.1%
3 4
 
< 0.1%
5.499 50
0.1%
6.9 52
0.1%
7 124
0.2%
8.9 78
0.1%
9.035 74
0.1%
9.079 51
0.1%
10 19
 
< 0.1%
ValueCountFrequency (%)
1100 1
 
< 0.1%
845 1
 
< 0.1%
600 1
 
< 0.1%
189 3
 
< 0.1%
142 321
0.5%
136.8 62
 
0.1%
124.9 14
 
< 0.1%
119.8 75
 
0.1%
117.4 10
 
< 0.1%
114.99 28
 
< 0.1%

Interactions

2024-12-02T15:42:41.288049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:03.035490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:09.434104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:17.560164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:25.048263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:29.942917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:34.727957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:42.955339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:03.532908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:09.988386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:18.182521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:25.401675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:30.241785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:35.177147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:44.747993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:03.930943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:10.364374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:18.691963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:25.787096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:30.606769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:35.832345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:46.801628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:04.313217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:11.036186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:19.234921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:26.139296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:30.955433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:36.475329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:48.709960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:04.669676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:11.640568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:19.811279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:26.431005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:31.199153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:37.159615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:50.439336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:05.016675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:12.344535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:20.263005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:26.916953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:31.457620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:37.910067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:52.367507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:05.410790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:12.815648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:20.849824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:27.381065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:31.805107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T15:42:38.355591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-02T15:43:27.824532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Base MSRPClean Alternative Fuel Vehicle (CAFV) EligibilityCountyDOL Vehicle IDElectric RangeElectric Vehicle TypeExpected Price ($1k)Legislative DistrictMakeModel YearStateZIP Code
Base MSRP1.0000.0240.0000.0260.0320.024-0.146-0.0040.249-0.1940.0000.004
Clean Alternative Fuel Vehicle (CAFV) Eligibility0.0241.0000.0680.3620.6710.7270.8900.0460.5920.4550.0000.000
County0.0000.0681.0000.0170.0560.1230.0520.5930.0450.0390.9361.000
DOL Vehicle ID0.0260.3620.0171.000-0.0560.090-0.062-0.0020.097-0.1480.006-0.008
Electric Range0.0320.6710.056-0.0561.0000.6330.0160.0350.450-0.3140.000-0.050
Electric Vehicle Type0.0240.7270.1230.0900.6331.0000.8460.0940.7960.2060.0000.000
Expected Price ($1k)-0.1460.8900.052-0.0620.0160.8461.0000.0740.8140.6210.000-0.153
Legislative District-0.0040.0460.593-0.0020.0350.0940.0741.0000.0610.0280.021-0.356
Make0.2490.5920.0450.0970.4500.7960.8140.0611.0000.4080.0000.000
Model Year-0.1940.4550.039-0.148-0.3140.2060.6210.0280.4081.0000.000-0.073
State0.0000.0000.9360.0060.0000.0000.0000.0210.0000.0001.0001.000
ZIP Code0.0040.0001.000-0.008-0.0500.000-0.153-0.3560.000-0.0731.0001.000

Missing values

2024-12-02T15:42:56.813689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-02T15:42:57.884396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDVIN (1-10)CountyCityStateZIP CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric UtilityExpected Price ($1k)
0EV331745YJ3E1EC6LSnohomishLYNNWOODWA98037.02020.0TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible308032.0109821694POINT (-122.287614 47.83874)PUGET SOUND ENERGY INC50
1EV40247JN1AZ0CP8BSkagitBELLINGHAMWA98229.02011.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible73040.0137375528POINT (-122.414936 48.709388)PUGET SOUND ENERGY INC15
2EV12248WBY1Z2C56FPierceTACOMAWA98422.02015.0BMWI3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible81027.0150627382POINT (-122.396286 47.293138)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY18
3EV557131G1RD6E44DKingREDMONDWA98053.02013.0CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible38045.0258766301POINT (-122.024951 47.670286)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)33.9
4EV287991G1FY6S05KPiercePUYALLUPWA98375.02019.0CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible238025.0296998138POINT (-122.321062 47.103797)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||ELMHURST MUTUAL POWER & LIGHT CO|PENINSULA LIGHT COMPANY41.78
5EV49859KMHE24L10GClarkVANCOUVERWA98683.02016.0HYUNDAISONATA PLUG-IN HYBRIDPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range273460018.0110121371POINT (-122.510748 45.603727)BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)16.365
7EV531215YJSA1E22GSpokaneSPOKANEWA99224.02016.0TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible21006.0349565044POINT (-117.505436 47.633834)MODERN ELECTRIC WATER COMPANY65
8EV468811N4BZ0CP9HKingBOTHELLWA98011.02017.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible10701.0327624048POINT (-122.197147 47.757791)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)19
9EV320041N4BZ0CP4GKingKENMOREWA98028.02016.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible84046.0148960990POINT (-122.246193 47.755504)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)27
10EV545031N4AZ1CP9LKingREDMONDWA98052.02020.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible149045.0132454822POINT (-122.122018 47.678465)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)40
IDVIN (1-10)CountyCityStateZIP CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric UtilityExpected Price ($1k)
64343EV734282C4RC1S79NKitsapBAINBRIDGE ISLANDWA98110.02022.0CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible32023.0189998441POINT (-122.534497 47.643688)PUGET SOUND ENERGY INC38
64344EV15745YJYGDEF6MKingSHORELINEWA98177.02021.0TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0032.0166439997POINT (-122.370159 47.743354)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)73
64345EV670895YJ3E1EA8NKingSEATTLEWA98144.02022.0TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0037.0183477598POINT (-122.30033 47.585339)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)64
64346EV24820YV4ED3UR6NKingSEATTLEWA98116.02022.0VOLVOXC40Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0034.0186988966POINT (-122.394511 47.574001)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)95
64347EV461111N4AZ0CP9DKitsapPOULSBOWA98370.02013.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible75023.0263375976POINT (-122.633393 47.748427)PUGET SOUND ENERGY INC18
64348EV6357KNDCE3LG7LKingSEATTLEWA98144.02020.0KIANIROBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible239037.0156575107POINT (-122.30033 47.585339)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)33
64349EV423JTDKN3DP2DPierceTACOMAWA98402.02013.0TOYOTAPRIUS PLUG-INPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range6027.0211048701POINT (-122.443211 47.252172)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY13.3
64350EV278521G1FX6S05JKingSEATTLEWA98119.02018.0CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible238036.0135543411POINT (-122.367721 47.639264)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)22.857
64351EV830WP1AE2A24HKingSEATTLEWA98115.02017.0PORSCHECAYENNEPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range14046.0192459907POINT (-122.297534 47.685291)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)45.7
64352EV111201N4BZ1CP8KLewisTOLEDOWA98591.02019.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible150020.0477551595POINT (-122.800917 46.444012)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PUD NO 1 OF LEWIS COUNTY35